In the Business Intelligence (BI) computer applications domain, business decision makers use analytical software to pose operational performance questions as queries against multi-dimensionally modeled business databases and data warehouses. These multi-dimensional models and analysis software tools are based on Online Analytic Processing (OLAP) concepts and technology. The analysis activity typically involves the creation and manipulation of a cross-tabular (also called “crosstab”) and/or graphical presentation of the data.
Large OLAP databases and multi-dimensionally modeled data warehouses typically contain large numbers of dimensional members or flat/non-existent dimensional hierarchies, or both. This is due to a variety of factors, including the volume of available and important data as a business operates and grows, the time constraints and computing resources required to stage and model the data warehouse and make it available for business decision-making processes, the need for flexible, unconstrained models for key business dimensions such as Customers and Time, or non-hierarchical models for inherently parent-child-relationship dimensions such as Invoices and Orders.
Multidimensional queries posed in this “large-OLAP” context often yield OLAP crosstabs containing a large amount of data. Trying to extract meaningful information from OLAP crosstabs becomes increasingly difficult as the size of the crosstab grows. The more data there is, the less likely it is that a user can learn anything useful from it.
The user may sift through data in a crosstab, and may delete or exclude information that is not relevant. However, such manual operations are tedious and prone to errors. If the data set is large enough, governors may be used to limit the size of the data represented in a crosstab. However, this does not give a full representation of all the data there is to examine.
It is therefore desirable to provide a mechanism for better management of large OLAP crosstabs.